code: def load_experiment(run_uuid: str, checkpoint: Optional[int] = None):
      """
      Load a saved experiment from [train model](train_model.html).
      """
  
      # Create configurations object
      conf = Configs()
      # Load custom configurations used in the experiment
      conf_dict = experiment.load_configs(run_uuid)
      # We need to get inputs to the feed forward layer, $f(c_i)$
      conf_dict["is_save_ff_input"] = True
  
      # This experiment is just an evaluation; i.e. nothing is tracked or saved
      experiment.evaluate()
      # Initialize configurations
      experiment.configs(conf, conf_dict)
      # Set models for saving/loading
      experiment.add_pytorch_models(get_modules(conf))
      # Specify the experiment to load from
      experiment.load(run_uuid, checkpoint)
  
      # Start the experiment; this is when it actually loads models
      experiment.start()
  
      return conf
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/knn/build_index.py
imports: from typing import Optional
  import faiss
  import numpy
  import torch
  from labml import experiment, monit, lab
  from labml.utils.pytorch import get_modules
  from labml_nn.transformers.knn.train_model import Configs
label_desc: Use image node_id 1 for calling a global function with example usage: load_experiment(run_uuid, checkpoint) and returns: conf